In critical situations the operators of complex industrial processes are often overloaded by a large amount of system information, e.g., a plurality of alarms. For example the alarms are caused by faults in different components usually monitored by sensors. Even in moderately complex systems it can be quite difficult to, reliably and quickly, find the root causes of the alarms, i.e. the locations of the faults. Thus today's complex industrial systems can be operated only thanks to advanced computer control systems. However, during normal operation, the operators are usually more or less redundant, while in fault or alarm situations they suddenly have to override the computerized control system and manually control the system. This change of state is often dramatic, since the operators are confronted with an unknown situation where plant state information is diffuse and the control monitors are flooded with alarms. Today's control systems offer very little help in such situations and there is a great risk for misunderstanding the new situation and consequently for taking sub-optimal or even wrong control measures.
The U.S. Pat. No. 5,914,875 discloses a method and an apparatus for diagnosing a plant using a plant model in an abstract function level based on a human cognitive process. However, this method and apparatus is limited to only one kind of fault diagnosis, namely to find the cause to a detected plant anomaly. Thus, it does not provide support to the operators in other tasks, such as detecting sensor faults, finding root causes in complex fault situations, predicting system behavior, and planning control actions. Furthermore, the method in U.S. Pat. No. 5,914,875 uses a simplified version of a multilevel flow model (MFM model). That is a model wherein a goal of a first network having a lower hierarchical level is connected to a second network having a higher hierarchical level and not to a function comprised in the second network, which makes the resolution of the fault diagnosis low. Thus it is possible to determine that a failed goal affects a network but not how the failed goal affects the functions inside the affected network.
In the disclosed method according to U.S. Pat. No. 5,914,875 a priority level is further assigned to the goals in the MFM model. According to the disclosed method, a failure propagation network is firstly detected, which failure propagation network comprises goals and flow structures having an abnormal state amount. Secondly, a flow structure at the lowest hierarchical level of a network having a top-goal with the highest priority level is selected to be diagnosed first, since it is considered that a failure propagates from a lower hierarchical level to a higher, and since that flow structure is considered to be functionally important and close to the origin of the anomaly. During the diagnosis of the flow structure, the state of elements comprised in the flow structure is determined. The determination is accomplished by searching the path from an element having a measured abnormal state to other elements having a measured abnormal state and assigning an abnormal state to the elements in between. However, if the path includes elements having a measured normal state, the elements between the abnormal element and the normal element are assigned a normal state. Thirdly, the abnormal elements of the selected flow structure is diagnosed using the state amounts of a set of elements determined or assumed as normal elements and by using mass balance or energy balance calculations to determine an estimation of the state amount of the abnormal elements. Further, if the state amount of the measured abnormal element is determined to be normal by means of the balance calculation, that element is considered to have a normal state. Thus, the method for finding the cause to a detected plant anomaly according to U.S. Pat. No. 5,914,875 is unnecessary computationally inefficient.